A novel approach for precision and recall estimation for star rating online customers based on positive movie reviews using naive bayes algorithm over K-nearest neighbour algorithm.

To estimate precision and recall for movie star rating using sentiment content. K-Nearest Neighbour with sample size of 10 and novel Naive Bayes with sample size of 10 was iterated at different times for predicting accuracy percentage of movie review. The F1 measure used in prediction to probabilities which helps to improve the prediction of accuracy percentage. The sigmoid function used in KNN prediction to probability which helps to improve the prediction of accuracy. Result: Results proved that Naive Bayes got significant results with 82% accuracy compared to KNN with 74% accuracy. There was a statistical significance between Naive Bayes and KNN (p=0.00) Naive Bayes is a simple and most effective algorithm to build fast machine learning models. Naive bayes with f1 measure helps in predicting with more accuracy percentage of movie review. [ABSTRACT FROM AUTHOR]